In a piece last year for PC Magazine, Rob Marvin wrote something that of course struck a chord with me. I say of course because of me being the pop culture savant that I am. He was writing about all this "stuff" and he used what I think is the perfect analogy.

"... predictive analytics is not like a crystal ball or Biff Tannen's sports almanac from Back to the Future 2. The algorithms and models can't tell your business beyond the shadow of a doubt that its next product will be a billion-dollar winner or that the market is about to tank. Data is still a means to make an educated guess; we're simply a lot better educated than we used to be."

In other words, we humans will still have a say and a role in all this; we simply cannot ever hand the keys over to a computer and go eat bonbons. I cannot stress how vitally important it is for marketers to never rely solely on a machine. Instinct and gut will always have a place along with all the AI and predictive analytics and so on.

There needs to be a balance - always.

So having said that you can take the following stat one of two ways: Based on a study by CLO and Raytheon, only 7% of learning organizations are leveraging the power of predictive analytics.

One way is to look at it from the perspective "It's good that only 7% are leveraging the power of predictive analytics" which could mean a great number are relying on gut and instinct.

But on the other hand "only 7% are leveraging the power of predictive analytics" which means a great number are not reaping the benefits.

Zoom In

One company that surely looks at the above stat via the "a great number are not reaping the benefits" angle is Zoomi, a learning analytics company that uses proprietary Artificial Intelligence to analyze each learner’s behavior, cognition, engagement and performance to predict learning and future performance, optimize learning content and to create a deep personalized individual and social learning experience.

I recently had the chance to chat with Jim Walker of Zoomi. Holding a Doctorate in Workplace Learning from Wharton I wanted to get his take on why artificial intelligence is key for learning, why eLearning provides more benefits than traditional training and other topics.

Steve Olenski: Why is AI so important when it comes to learning?

Jim Walker: AI is important in any situation where we want to scale up or speed up tasks that normally require human intelligence to complete.

It has the added benefit of completing these tasks consistently, and in many cases, can achieve even better performance than humans can. Moreover, AI can uncover patterns of human behavior, cognition, engagement and performance that humans can’t discern.

AI can help predict learning and performance, create deep personalization based on individual preferences and identify places where courses need to be optimized or improved.

Now, consider learning: Even if we had all the will in the world, a company of 100,000 employees could not maintain a staff to individually train all employees and give them a consistent experience. Computer-based training, where the content is delivered electronically rather than in-person, has given training consistency at scale, but in doing so, the responsiveness and adaptability of the instructor was lost. By employing AI, we have brought back the intelligence and flexibility of an in-person instructor while keeping the scalability and consistency of the computer based delivery.

Olenski: Why is eLearning more beneficial than traditional face-to-face training?

Walker: eLearning works in concert with traditional face-to-face training. The process of training specific, unique skills will still probably benefit from having a seasoned practitioner face to face with the employee or learner. Technologies like virtual reality simulations and sentiment analysis are reducing the examples where this is true, though. But what you certainly get from eLearning, which you can’t replicate in-person, are the consistent quality of delivery and the ability to gather large amounts of data about learners simultaneously.

I guarantee you that when I send 10 live trainers into the field, on day one their audiences are getting 10 different experiences. If an instructor is having an off day, or if they have subtle misunderstandings on any of the content, that misunderstanding will be propagated in every subsequent classroom.

With eLearning, you can be confident of exactly what experience your trainees are getting, you can make like for like inferences about how well that product is performing in the workplace, and you can learn about how employees are learning by capturing their behavior, cognition, engagement and performance and using AI to analyze this data.

When the data tells you to make a change to the course, you can do that on the fly, without having to take trainers off the road, fly them back to HQ and retool them. Elearning can be tailored to each individual rather than a one-size-fits all instructor approach.

Olenski: According to Deloitte 84% of global executives ranked employee learning as important or very important and better learning opportunities are one of the largest drivers of employee engagement and a strong workplace culture. So why then do we also see stats i.e. 40% of employees who receive poor job training leave their positions within the first year?

Walker: Learning and development seems to be the last corner of the organization where we have been happy to fly blind and operate without meaningful data. We haven’t done this well at all. We give people surveys, we ask them about the impact they expect their training to have on their productivity, but really, we have had no credible way to gauge the efficacy of, and by extension the return on, learning.

This is why it is so crucial that we devise meaningful metrics by which training can be evaluated. Zoomi takes the approach of looking at the learner’s experience directly. How engaged are they with the material? What cognitive and behavioral preferences are they displaying?

What learning strategies can we tell they are employing based on their sequences of actions? What are the measurable outcomes in the workplace that the training was targeting? We have already seen the upswing possible with this approach when we have used behavior learners exhibited while consuming content to predict changes in operational metrics in the workplace. We have found ways to reduce the time required for learning and to improve quiz results.

We have seen improved satisfaction, and have avoided frustration. And most importantly, the business results have been impacted, which creates a virtuous circle whereby learning content continues to improve, which continues to drive operational outcomes.